Abstract
The exposure of carbon-fiber-reinforced polymers (CFRPs) to open-field conditions was investigated. Establishment of structure–property relations with nanoindentation enabled the observation of modification effects on carbon-fiber interfaces, and impact resistance. Mapping of nanomechanical properties was performed using expectation-maximization optimization of Gaussian fitting for each CFRPs microstructure (matrix, interface, carbon fiber), while Weibull analysis connected the weathering effect to the statistically representative behavior of the produced composites. Plasma modification demonstrated reduced defect density and improved nanomechanical properties after weathering. Artificial intelligence for anomaly detection provided insights on condition monitoring of CFRPs. Deep-learning neural networks with three hidden layers were used to model the resistance to plastic deformation based on nanoindentation parameters. This study provides new assessment insights in composite engineering and quality assurance, especially during exposure under service conditions.
Highlights
Carbon-Fiber (CFs)-reinforced Polymers (CFRPs) are on the peak of their development, and are expected to be utilized massively in aerospace, automotive, and construction markets as substitutes to metal compartments [1,2,3,4,5,6,7,8,9,10]
Even after 1000 h of weathering, this value was higher than the weathered neat carbon-fiber-reinforced polymers (CFRPs) after the same duration by 126.2%, while it exceeded by 14.3% its previous condition
Chemical affinity of surficial chemistry of the fiber and matrix in the interface region can be transformed to chemical bonding upon exposure to several thermal cycles during exposure at service conditions. These intrinsic changes are expected to enhance homogeneity of reinforcement zones by post-curing reactions [3,24]. This was confirmed with machine learning, which demonstrated a minor degradation of 2.9% based on the model trained with pristine data, while it became equal to 1.8%, when using polymethacrylic acid (PMAA) CFRPs for training, which could be considered as statistically insignificant compared to the weathered pristine and atmospheric pressure plasma (APP) specimens
Summary
Carbon-Fiber (CFs)-reinforced Polymers (CFRPs) are on the peak of their development, and are expected to be utilized massively in aerospace, automotive, and construction markets as substitutes to metal compartments [1,2,3,4,5,6,7,8,9,10]. Crack generation and propagation due to different thermal expansion coefficient of the matrix and CF phases, and brinelling during testing (i.e., indentation) can introduce irregularities in the identified mechanical properties [30], termed as anomalies In this direction, it is highly desirable to identify the failure-mode characteristics in order to use data-acquisition techniques to detect these faults in a component fingerprint. It is highly desirable to identify the failure-mode characteristics in order to use data-acquisition techniques to detect these faults in a component fingerprint This may be a key step to introducing unsupervised estimators and qualitatively assessing [28] ageing using nanoindentation testing and machine learning for prognosis. Order to identify and assess the anomalies based on mean square error values, which were introduced to the CFRP structure by weathering
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